Rules/model-based data processing system for intelligent event prediction in an electronic data interchange system

Abstract

A system for electronic data interchange (EDI) management includes a memory for storing the EDI document data and a machine learning model representing a set of features of EDI documents and a corresponding status. The system further includes a processor and a non-transitory computer readable medium storing instructions for: accessing an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document; and translating the EDI file into a first translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the first translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by the machine learning model to determine a status of the first EDI document.

What is claimed is: 
     
       1. A system for electronic data interchange (EDI) management comprising:
 a memory for storing EDI document data and a machine learning model representing a set of features of EDI documents and a corresponding status; 
 a processor; 
 a non-transitory computer readable medium storing thereon a set of computer executable instructions, the set of computer executable instructions comprising instructions for:
 accessing an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document; and 
 translating the EDI file into a first translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the first translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by the machine learning model to determine a status of the first EDI document. 
 
 
     
     
       2. The system of  claim 1 , wherein the first translated EDI document is a JSON document. 
     
     
       3. The system of  claim 1 , wherein the hierarchical structure comprises a segment and data elements from the segment extracted from the first EDI document and arranged with the data elements positioned as children of the segment in the first translated EDI document. 
     
     
       4. The system of  claim 3 , wherein each data element from the segment is named in the first translated EDI document based on a name of the segment and a position of the data element. 
     
     
       5. The system of  claim 3 , wherein the segment is in a loop in the first EDI document and positioned as a child of a loop start segment in the first translated EDI document. 
     
     
       6. The system of  claim 5 , wherein the loop is nested loop. 
     
     
       7. The system of  claim 3 , wherein the set of computer executable instructions further comprises instructions executable to extract the set of features and generate a feature vector from the first translated EDI document according to a feature mapping rule that specifies which segments and data elements are to be transformed into features. 
     
     
       8. A computer program product comprising a non-transitory, computer-readable medium storing a set of computer instructions executable by a computer, the set of computer instructions comprising instructions for:
 accessing an electronic data interchange (EDI) file, the EDI file comprising envelope metadata for an envelope and a first EDI document; and 
 translating the EDI file into a translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by a machine learning model that represents a set of features of EDI documents and a corresponding status to determine a status of the first EDI document. 
 
     
     
       9. The computer program product of  claim 8 , wherein the translated EDI document is a JSON document. 
     
     
       10. The computer program product of  claim 8 , wherein the hierarchical structure comprises a segment and data elements from the segment extracted from the first EDI document and arranged with the data elements positioned as children of the segment in the translated EDI document. 
     
     
       11. The computer program product of  claim 10 , wherein each data element from the segment is named in the translated EDI document based on a name of the segment and a position of the data element. 
     
     
       12. The computer program product of  claim 10 , wherein the segment is in a loop in the first EDI document and positioned as a child of a loop start segment in the translated EDI document. 
     
     
       13. The computer program product of  claim 12 , wherein the loop is nested loop. 
     
     
       14. The computer program product of  claim 11 , wherein the set of computer executable instructions further comprises instructions executable to extract the set of features and generate a feature vector from the translated EDI document according to a feature mapping rule that specifies which segments and data elements are to be transformed into features. 
     
     
       15. A method for an electronic data interchange (EDI) document processing comprising:
 receiving an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document; 
 translating the EDI file into a translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by a machine learning model that represents a set of features of EDI documents and a corresponding status; and 
 determining a status of the first EDI document using the machine learning model. 
 
     
     
       16. The method of  claim 15 , wherein the translated EDI document is a JSON document. 
     
     
       17. The method of  claim 15 , wherein the hierarchical structure comprises a segment and data elements from the segment extracted from the first EDI document and arranged with the data elements positioned as children of the segment in the translated EDI document. 
     
     
       18. The method of  claim 17 , wherein each data element from the segment is named in the translated EDI document based on a name of the segment and a position of the data element. 
     
     
       19. The method of  claim 17 , wherein the segment is in a loop in the first EDI document and positioned as a child of a loop start segment in the translated EDI document. 
     
     
       20. The method of  claim 19 , wherein the loop is nested loop. 
     
     
       21. The method of  claim 17 , further comprising extracting the set of features and generating a feature vector from the translated EDI document according to a feature mapping rule that specifies which segments and data elements are to be transformed into features.